The present invention relates to behavior modeling and how to detect, or predict, an occurrence of adverse events based on observed changes in a behavior.
Elderly people suffer from a number of age-related health problems. These include, but are not limited to, diminished visual acuity, difficulty with hearing, impairment of tactile senses, short and long term memory loss, lack of stability resulting in frequent falls, other chronic conditions, etc. All of these problems result in serious concerns regarding the safety of elderly people living at home, particularly if they are living alone. Many studies have shown the benefits of getting help quickly after certain types of adverse events. For example, in the case of falls, getting help within one hour is widely believed to substantially reduce risk of both hospitalization and death.
Over a long period of time, there have been numerous attempts in the arts to address this long-standing problem related to elder care by technological means. Early monitoring systems employed a pendent or wristband worn by the person being monitored that contained a medical alarm button. When the wearer pressed the button on the pendent, it sent a signal to a base station connected to a call center by means of the public telephone network.
Devices to detect unusual behaviors, including behaviors that may be hazardous or indicate a bad outcome of some condition, continued to evolve. Wearable sensors were added to detect falling events, for example. Some systems include sensors to detect vital signs such as pulse, heartbeat, and temperature.
Another approach is using passive sensors in the home to detect critical events. Using this approach does not require active participation by the user; the person monitored is simply free to go about their daily activities without having to change their routines.
Other approaches detect isolated acts or behavior patterns through the use of motion sensors and/or sensors linked to different articles in the household such as light switches, door locks, toilets etc.
Another technique for passive sensing is to use cameras and different methods for recognizing patterns of behavior.
U.S. Pat. No. 6,095,985 describes a known system that directly monitors the health of a patient as opposed to indirectly, or behaviorally, detecting a medical problem. Rather, it does so by employing a set of physiological sensors placed on the patient's body.
A number of patents, such as U.S. Pat. Nos. 7,586,418, 7,589,637, and 7,905,832 merely monitor activity, as an attribute having a binary value, during various times of day. The assumption is that if the patient is in motion during appropriate times of the day and not in motion during the night, then no medical problem exists. In such systems, if the patient takes a nap during the day or gets up to go to the bathroom at night, a false alarm will be generated. Another patent, U.S. Pat. No. 8,223,011 describes a system wherein for each patient predetermined rules are established for each daily block of time and place within the residence. All of the patents referred to above require some a priori knowledge of the patient, the patient's habits, and/or the patient's environment, either for determining individual habits or for setting detection and/or significance thresholds for sensors or processed sensor outputs.
A number of other systems described in U.S. patents add some degree of adaptive learning to help construct a behavior profile. For example, U.S. Pat. No. 7,552,030 describes an adaptive learning method to generate a behavior model. The method is shown to generate specific individual behavior models for specific predetermined actions such as opening a refrigerator door. Another patent, U.S. Pat. No. 7,847,682 describes a system that senses abnormal signs from a daily activity sequence by using a preset sequence alignment algorithm and comparing a sequence alignment value obtained by the system with a threshold value. Other systems described in U.S. patents, such as those described in U.S. Pat. Nos. 7,202,791 and 7,369,680, employ video cameras to generate a graphic image from which feature extraction algorithms are employed to use as a basis for building up a behavior profile. The systems and methods described define vertical distance, horizontal distance, time, body posture and magnitude of body motion as the features to be extracted from the video image.